Mock sample for your project: NetworkManagementClient API

Storage operation results.

ARM Databricks

Azure Maintenance Management Client

Update location operation endpoints and objects.

Region health operation endpoints and objects.

The SAP HANA on Azure Management Client.

Use these REST APIs for performing operations on the ApiVersionSet entity associated with your Azure API Management deployment. Using this entity you create and manage API Version Sets that are used to group APIs for consistent versioning.

Deployment Admin Client.

Use these REST APIs for performing retrieving a collection of policy snippets available in Azure API Management deployment.

The Admin Storage Management Client.

ARM Databricks

The Turbine Labs API provides CRUD operations for core object types, and is
mostly RESTy. The easiest way to interact with the API is with
tbnctl.
If you want to make direct HTTP calls, however, you can obtain an access
token using tbnctl, and then pass it in the Authorization header,
prefixed by Token :

The service is used to perform ink layout and recognition of written words and shapes. Ink strokes passed to the service are recognized and organized into recognition results in the response

The Anomaly Detector API detects anomalies automatically in time series data. It supports two kinds of mode, one is for stateless using, another is for stateful using. In stateless mode, there are three functionalities. Entire Detect is for detecting the whole series with model trained by the time series, Last Detect is detecting last point with model trained by points before. ChangePoint Detect is for detecting trend changes in time series. In stateful mode, user can store time series, the stored time series will be used for detection anomalies. Under this mode, user can still use the above three functionalities by only giving a time range without preparing time series in client side. Besides the above three functionalities, stateful model also provide group based detection and labeling service. By leveraging labeling service user can provide labels for each detection result, these labels will be used for retuning or regenerating detection models. Inconsistency detection is a kind of group based detection, this detection will find inconsistency ones in a set of time series. By using anomaly detector service, business customers can discover incidents and establish a logic flow for root cause analysis.

The HDInsight Management Client.

An API for face detection, verification, and identification.